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Lead Applied Scientist

2 months ago

Seattle, Washington, United States Amazon Services LLC Full time

Principal Applied Scientist - Prime Video Core Playback

Become a vital part of our Prime Video team, where we are transforming the entertainment landscape by providing unmatched options and flexibility. Enjoy Prime Originals, movies, TV shows, live sports, and a variety of add-on subscriptions, all tailored to your preferences without the constraints of long-term commitments.

The Core Playback division is pivotal to Prime Video's global streaming capabilities. We are on the lookout for a Principal Applied Scientist to spearhead our scientific initiatives across diverse domains, collaborating with multidisciplinary teams to foster advancements in applied machine learning.

Key Responsibilities:
  • Possess extensive knowledge in applied machine learning
  • Partner with teams to convert requirements into actionable deliverables
  • Propel innovation in AI and ML to improve customer satisfaction and business performance
  • Focus on enhancing the streaming experience, deriving insights from data, and refining software development methodologies
Basic Qualifications:
  • Over 10 years of experience in the technology sector
  • Proven experience collaborating with science, data processing, and software engineering teams
  • Advanced degree in Computer Science or a related discipline
  • Strong expertise in developing real-time systems utilizing AI, ML, and NLP
  • Demonstrated success in delivering large-scale, cross-functional projects
  • Proficient in data structures, algorithms, and machine learning techniques
  • Excellent customer engagement skills and a solid record of scientific publications
Preferred Qualifications:
  • PhD in Computer Science or a related field
  • Specialized knowledge in advanced machine learning areas
  • Experience with large language models and collaborative academic projects
  • Outstanding technical communication abilities
  • More than 15 years of experience in developing machine learning models